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  • Item type:Publication,
    Cohesive subgroups in academic networks: Unveiling clique integration of top-level female and male researchers Open PDF 
    (2015)
    Kegen, N.V.
    Social networks are said to have a positive impact on scientific development. Conventionally, it is argued that female and male researchers differ in access to and participation in networks and hence experience unequal career opportunities. Due to limited capacities of time and resources as well as homophily, top-level scientists may structure their contacts to reduce problems of complexity and uncertainty. The outcomes of the structuring can be cohesive subgroups within networks of relation. Women in science might suffer exclusion from cliques because of being dissimilar in the arena. The present paper aims to explore integration in and composition of scientific cliques. A three-step analysis is conducted: Firstly, cliques are identified. Secondly, overlap structures are examined. Thirdly, group compositions are analysed in terms of other personal attributes of the researchers involved. Building on network data of female and male investigators, the article applies a comparative case study design including two cutting edge research institutions from the German Excellence Initiative. The study contrasts a Cluster of Excellence with a Graduate School and the corresponding formal with the informal networks. The results imply that the general hypothesis of unfavourably embedded female researchers cannot be supported. Although women are less integrated in scientific cliques, the majority is involved in an inner social circle which enables access to career-relevant network resources. © 2015, Kluwer Academic Publishers.
  • Item type:Publication,
    Gender Differences in Collaboration Patterns in Computer Science Open PDF 
    (2022)
    Yamamoto, J.
    ;
    Frachtenberg, E.
    The research discipline of computer science (CS) has a well-publicized gender disparity. Multiple studies estimate the ratio of women among publishing researchers to be around 15–30%. Many explanatory factors have been studied in association with this gender gap, including differences in collaboration patterns. Here, we extend this body of knowledge by looking at differences in collaboration patterns specific to various fields and subfields of CS. We curated a dataset of nearly 20,000 unique authors of some 7000 top conference papers from a single year. We manually assigned a field and subfield to each conference and a gender to most researchers. We then measured the gender gap in each subfield as well as five other collaboration metrics, which we compared to the gender gap. Our main findings are that the gender gap varies greatly by field, ranging from 6% female authors in theoretical CS to 42% in CS education; subfields with a higher gender gap also tend to exhibit lower female productivity, larger coauthor groups, and higher gender homophily. Although women published fewer single-author papers, we did not find an association between single-author papers and the ratio of female researchers in a subfield. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
  • Item type:Publication,
    Representation of women in HPC conferences
    (2021)
    Frachtenberg, E.
    ;
    Kaner, R.D.
    Women are acutely underrepresented in the HPC workforce. Addressing this gap requires accurate metrics on the representation of women and its associated factors. The goal of this paper is to provide current, broad, and reproducible data on this gender gap. Specifically, this study provides in-depth statistics on women s representation in HPC conferences, especially for authors of peer-reviewed papers, who serve as the keystone for future advances in the field. To this end, we analyzed participant data from nine HPC and HPC-related peer-reviewed conferences. In addition to gender distributions, we looked at post-publication citation statistics of the papers and authors research experience, country, and work sector. Our main finding is that women represent only 10% of all HPC authors, with large geographical variations and small variations by sector. Representation is particularly low at higher experience levels. This 10% ratio is lower than even the 20 30% ratio in all computer science. © 2021 IEEE Computer Society. All rights reserved.
  • Item type:Publication,
    Gender and authorship patterns in urban land science Open PDF 
    (2022)
    Chen, T.-H.K.
    ;
    Seto, K.C.
    What are patterns of gender and authorship in urban land science? Our bibliometric analysis shows that the proportion of women shrinks among highly productive, impactful, and senior authors, akin to a pyramid shape. First, women are only one in ten researchers with an h-index above the 95th percentile. Second, women are first authors on 20% of all influential papers cited more than one hundred times. Third, women publish less frequently (1.6 papers/year) than men (2.2). Fourth, women have shorter career lengths (9.4 years) than men (11.8). Since the 2000s, citation rates for women and men have converged. For the generation starting careers since 2016, the proportion of women with an h-index above the 90th percentile increased to 25%. During the Covid-19 pandemic, there was a 51% increase in productivity for women. Despite these changes, gender disparities in urban land science are most pronounced among the most productive and impactful authors. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
  • Item type:Publication,
    A Longitudinal View of Gender Balance in a Large Computer Science Program
    (2020)
    Baer, A
    ;
    DeOrio, A
    ;
    ASSOC COMP MACHINERY
    Computer Science has a persistent lack of women's participation. In order to best effect change, we require a more fine-grain analysis of the gender disparity as it changes throughout an undergraduate Computer Science curriculum. In this paper, we use a quantitative approach to highlight, with greater specificity, the point in an undergraduate career where gender balance changes. We also examine the role of grades in students' decisions to stay in the course sequence. Our goal is to enable targeted interventions that will make Computer Science a more welcoming discipline. Our study examines 30,890 unique student records over ten years at a large, public research institution. The records include students who took a Computer Science course over the past ten years. The dataset contains information about gender, majors, minors, academic level, and GPA. The dataset also includes a record from each course taken by each student and their final grade. We observed a modest increase in women's participation in all Computer Science courses over the past ten years. Despite this increase, the gender disparity is still large. Through our analysis, we found that women consistently choose not to continue through the Computer Science sequence at a higher rate than men. This higher attrition could be linked to women receiving lower grades in most introductory CS courses despite having the same or higher GPAs than men. Our results reveal specific areas where intervention can be the most effective in changing the stubborn gender disparity in Computer Science.